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Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

Department of Mobility and Energy, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
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Appl. Sci. 2020, 10(18), 6270; https://doi.org/10.3390/app10186270
Received: 30 July 2020 / Revised: 3 September 2020 / Accepted: 4 September 2020 / Published: 10 September 2020
(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)
Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%. View Full-Text
Keywords: vehicular tracking; traffic analysis; congestion detection; road cameras; machine learning; computer vision vehicular tracking; traffic analysis; congestion detection; road cameras; machine learning; computer vision
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Sonnleitner, E.; Barth, O.; Palmanshofer, A.; Kurz, M. Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data. Appl. Sci. 2020, 10, 6270.

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